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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="preprocessor">#ifndef CAFFE_INFOGAIN_LOSS_LAYER_HPP_</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="preprocessor">#define CAFFE_INFOGAIN_LOSS_LAYER_HPP_</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;</div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="preprocessor">#include &lt;vector&gt;</span></div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;</div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="preprocessor">#include &quot;caffe/blob.hpp&quot;</span></div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="preprocessor">#include &quot;caffe/layer.hpp&quot;</span></div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="preprocessor">#include &quot;caffe/proto/caffe.pb.h&quot;</span></div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;</div><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="preprocessor">#include &quot;caffe/layers/loss_layer.hpp&quot;</span></div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="preprocessor">#include &quot;caffe/layers/softmax_layer.hpp&quot;</span></div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;</div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacecaffe.html">caffe</a> {</div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;</div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00048"></a><span class="lineno"><a class="line" href="classcaffe_1_1InfogainLossLayer.html">   48</a></span>&#160;<span class="keyword">class </span><a class="code" href="classcaffe_1_1InfogainLossLayer.html">InfogainLossLayer</a> : <span class="keyword">public</span> <a class="code" href="classcaffe_1_1LossLayer.html">LossLayer</a>&lt;Dtype&gt; {</div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160; <span class="keyword">public</span>:</div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;  <span class="keyword">explicit</span> <a class="code" href="classcaffe_1_1InfogainLossLayer.html">InfogainLossLayer</a>(<span class="keyword">const</span> LayerParameter&amp; param)</div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;      : <a class="code" href="classcaffe_1_1LossLayer.html">LossLayer&lt;Dtype&gt;</a>(param), infogain_() {}</div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classcaffe_1_1InfogainLossLayer.html#a772be3f4074c72b3cf9214bda3422402">LayerSetUp</a>(<span class="keyword">const</span> vector&lt;<a class="code" href="classcaffe_1_1Blob.html">Blob&lt;Dtype&gt;</a>*&gt;&amp; bottom,</div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;      <span class="keyword">const</span> vector&lt;<a class="code" href="classcaffe_1_1Blob.html">Blob&lt;Dtype&gt;</a>*&gt;&amp; top);</div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classcaffe_1_1InfogainLossLayer.html#a83ed478450bc7f629499fed37f654c5c">Reshape</a>(<span class="keyword">const</span> vector&lt;<a class="code" href="classcaffe_1_1Blob.html">Blob&lt;Dtype&gt;</a>*&gt;&amp; bottom,</div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;      <span class="keyword">const</span> vector&lt;<a class="code" href="classcaffe_1_1Blob.html">Blob&lt;Dtype&gt;</a>*&gt;&amp; top);</div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;</div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;  <span class="comment">// InfogainLossLayer takes 2-3 bottom Blobs; if there are 3 the third should</span></div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;  <span class="comment">// be the infogain matrix.  (Otherwise the infogain matrix is loaded from a</span></div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;  <span class="comment">// file specified by LayerParameter.)</span></div><div class="line"><a name="l00060"></a><span class="lineno"><a class="line" href="classcaffe_1_1InfogainLossLayer.html#aa03732f381764180748479c83b289869">   60</a></span>&#160;  <span class="keyword">virtual</span> <span class="keyword">inline</span> <span class="keywordtype">int</span> <a class="code" href="classcaffe_1_1InfogainLossLayer.html#aa03732f381764180748479c83b289869">ExactNumBottomBlobs</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> -1; }</div><div class="line"><a name="l00061"></a><span class="lineno"><a class="line" href="classcaffe_1_1InfogainLossLayer.html#ad8a1ef702a695e379e5d0450369b4a0c">   61</a></span>&#160;  <span class="keyword">virtual</span> <span class="keyword">inline</span> <span class="keywordtype">int</span> <a class="code" href="classcaffe_1_1InfogainLossLayer.html#ad8a1ef702a695e379e5d0450369b4a0c">MinBottomBlobs</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> 2; }</div><div class="line"><a name="l00062"></a><span class="lineno"><a class="line" href="classcaffe_1_1InfogainLossLayer.html#a9b2372959a16da1e80ae7a98b7689a4c">   62</a></span>&#160;  <span class="keyword">virtual</span> <span class="keyword">inline</span> <span class="keywordtype">int</span> <a class="code" href="classcaffe_1_1InfogainLossLayer.html#a9b2372959a16da1e80ae7a98b7689a4c">MaxBottomBlobs</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> 3; }</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;  <span class="comment">// InfogainLossLayer computes softmax prob internally.</span></div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;  <span class="comment">// optional second &quot;top&quot; outputs the softmax prob</span></div><div class="line"><a name="l00066"></a><span class="lineno"><a class="line" href="classcaffe_1_1InfogainLossLayer.html#aaf55e75f2296586b1fee0175e2d72fbb">   66</a></span>&#160;  <span class="keyword">virtual</span> <span class="keyword">inline</span> <span class="keywordtype">int</span> <a class="code" href="classcaffe_1_1InfogainLossLayer.html#aaf55e75f2296586b1fee0175e2d72fbb">ExactNumTopBlobs</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> -1; }</div><div class="line"><a name="l00067"></a><span class="lineno"><a class="line" href="classcaffe_1_1InfogainLossLayer.html#a15c4916e5de27151eb745491d8d14d41">   67</a></span>&#160;  <span class="keyword">virtual</span> <span class="keyword">inline</span> <span class="keywordtype">int</span> <a class="code" href="classcaffe_1_1InfogainLossLayer.html#a15c4916e5de27151eb745491d8d14d41">MinTopBlobs</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> 1; }</div><div class="line"><a name="l00068"></a><span class="lineno"><a class="line" href="classcaffe_1_1InfogainLossLayer.html#a93019601c6256354fd4758da91d9311f">   68</a></span>&#160;  <span class="keyword">virtual</span> <span class="keyword">inline</span> <span class="keywordtype">int</span> <a class="code" href="classcaffe_1_1InfogainLossLayer.html#a93019601c6256354fd4758da91d9311f">MaxTopBlobs</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> 2; }</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;</div><div class="line"><a name="l00070"></a><span class="lineno"><a class="line" href="classcaffe_1_1InfogainLossLayer.html#aada26ffd60207582fe2af602004e271b">   70</a></span>&#160;  <span class="keyword">virtual</span> <span class="keyword">inline</span> <span class="keyword">const</span> <span class="keywordtype">char</span>* <a class="code" href="classcaffe_1_1InfogainLossLayer.html#aada26ffd60207582fe2af602004e271b">type</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;InfogainLoss&quot;</span>; }</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160; <span class="keyword">protected</span>:</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classcaffe_1_1InfogainLossLayer.html#a134b51c126eb4b62fac804965f8d8327">Forward_cpu</a>(<span class="keyword">const</span> vector&lt;<a class="code" href="classcaffe_1_1Blob.html">Blob&lt;Dtype&gt;</a>*&gt;&amp; bottom,</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;      <span class="keyword">const</span> vector&lt;<a class="code" href="classcaffe_1_1Blob.html">Blob&lt;Dtype&gt;</a>*&gt;&amp; top);</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classcaffe_1_1InfogainLossLayer.html#a6d8fc17daa7233fb96629b641fbc46ac">Backward_cpu</a>(<span class="keyword">const</span> vector&lt;<a class="code" href="classcaffe_1_1Blob.html">Blob&lt;Dtype&gt;</a>*&gt;&amp; top,</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;      <span class="keyword">const</span> vector&lt;bool&gt;&amp; propagate_down, <span class="keyword">const</span> vector&lt;<a class="code" href="classcaffe_1_1Blob.html">Blob&lt;Dtype&gt;</a>*&gt;&amp; bottom);</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;  <span class="keyword">virtual</span> Dtype <a class="code" href="classcaffe_1_1InfogainLossLayer.html#a0e5e9667b19fb88ece7298e3e83d2fdb">get_normalizer</a>(</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;      LossParameter_NormalizationMode normalization_mode, <span class="keywordtype">int</span> valid_count);</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;  <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classcaffe_1_1InfogainLossLayer.html#a030296e6af30acd17a3cfe4463456147">sum_rows_of_H</a>(<span class="keyword">const</span> <a class="code" href="classcaffe_1_1Blob.html">Blob&lt;Dtype&gt;</a>* H);</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;</div><div class="line"><a name="l00122"></a><span class="lineno"><a class="line" href="classcaffe_1_1InfogainLossLayer.html#a1992ffcac64ab6d61ebce80c7d9e8405">  122</a></span>&#160;  shared_ptr&lt;Layer&lt;Dtype&gt; &gt; <a class="code" href="classcaffe_1_1InfogainLossLayer.html#a1992ffcac64ab6d61ebce80c7d9e8405">softmax_layer_</a>;</div><div class="line"><a name="l00124"></a><span class="lineno"><a class="line" href="classcaffe_1_1InfogainLossLayer.html#a4ae881c2950ca84a50b0f964797defd6">  124</a></span>&#160;  <a class="code" href="classcaffe_1_1Blob.html">Blob&lt;Dtype&gt;</a> <a class="code" href="classcaffe_1_1InfogainLossLayer.html#a4ae881c2950ca84a50b0f964797defd6">prob_</a>;</div><div class="line"><a name="l00126"></a><span class="lineno"><a class="line" href="classcaffe_1_1InfogainLossLayer.html#ad919452e3fbb182cf56ded1e32ee001d">  126</a></span>&#160;  vector&lt;Blob&lt;Dtype&gt;*&gt; <a class="code" href="classcaffe_1_1InfogainLossLayer.html#ad919452e3fbb182cf56ded1e32ee001d">softmax_bottom_vec_</a>;</div><div class="line"><a name="l00128"></a><span class="lineno"><a class="line" href="classcaffe_1_1InfogainLossLayer.html#abce0ff34e57ed3660e39633535c97e41">  128</a></span>&#160;  vector&lt;Blob&lt;Dtype&gt;*&gt; <a class="code" href="classcaffe_1_1InfogainLossLayer.html#abce0ff34e57ed3660e39633535c97e41">softmax_top_vec_</a>;</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;  <a class="code" href="classcaffe_1_1Blob.html">Blob&lt;Dtype&gt;</a> infogain_;</div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;  <a class="code" href="classcaffe_1_1Blob.html">Blob&lt;Dtype&gt;</a> sum_rows_H_;  <span class="comment">// cache the row sums of H.</span></div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;</div><div class="line"><a name="l00134"></a><span class="lineno"><a class="line" href="classcaffe_1_1InfogainLossLayer.html#a421720fc0f85daf8b6b7808719b1f9e8">  134</a></span>&#160;  <span class="keywordtype">bool</span> <a class="code" href="classcaffe_1_1InfogainLossLayer.html#a421720fc0f85daf8b6b7808719b1f9e8">has_ignore_label_</a>;</div><div class="line"><a name="l00136"></a><span class="lineno"><a class="line" href="classcaffe_1_1InfogainLossLayer.html#ad3f7c2efdf32f99510186495ba7c5cff">  136</a></span>&#160;  <span class="keywordtype">int</span> <a class="code" href="classcaffe_1_1InfogainLossLayer.html#ad3f7c2efdf32f99510186495ba7c5cff">ignore_label_</a>;</div><div class="line"><a name="l00138"></a><span class="lineno"><a class="line" href="classcaffe_1_1InfogainLossLayer.html#ab7fe88c996d31d67f5e13fc0ffc803c2">  138</a></span>&#160;  LossParameter_NormalizationMode <a class="code" href="classcaffe_1_1InfogainLossLayer.html#ab7fe88c996d31d67f5e13fc0ffc803c2">normalization_</a>;</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;</div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;  <span class="keywordtype">int</span> infogain_axis_, outer_num_, inner_num_, num_labels_;</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;};</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;</div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;}  <span class="comment">// namespace caffe</span></div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;<span class="preprocessor">#endif  // CAFFE_INFOGAIN_LOSS_LAYER_HPP_</span></div><div class="ttc" id="classcaffe_1_1InfogainLossLayer_html_a15c4916e5de27151eb745491d8d14d41"><div class="ttname"><a href="classcaffe_1_1InfogainLossLayer.html#a15c4916e5de27151eb745491d8d14d41">caffe::InfogainLossLayer::MinTopBlobs</a></div><div class="ttdeci">virtual int MinTopBlobs() const</div><div class="ttdoc">Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...</div><div class="ttdef"><b>Definition:</b> infogain_loss_layer.hpp:67</div></div>
<div class="ttc" id="classcaffe_1_1InfogainLossLayer_html_a772be3f4074c72b3cf9214bda3422402"><div class="ttname"><a href="classcaffe_1_1InfogainLossLayer.html#a772be3f4074c72b3cf9214bda3422402">caffe::InfogainLossLayer::LayerSetUp</a></div><div class="ttdeci">virtual void LayerSetUp(const vector&lt; Blob&lt; Dtype &gt; *&gt; &amp;bottom, const vector&lt; Blob&lt; Dtype &gt; *&gt; &amp;top)</div><div class="ttdoc">Does layer-specific setup: your layer should implement this function as well as Reshape. </div><div class="ttdef"><b>Definition:</b> infogain_loss_layer.cpp:12</div></div>
<div class="ttc" id="classcaffe_1_1InfogainLossLayer_html_ad919452e3fbb182cf56ded1e32ee001d"><div class="ttname"><a href="classcaffe_1_1InfogainLossLayer.html#ad919452e3fbb182cf56ded1e32ee001d">caffe::InfogainLossLayer::softmax_bottom_vec_</a></div><div class="ttdeci">vector&lt; Blob&lt; Dtype &gt; * &gt; softmax_bottom_vec_</div><div class="ttdoc">bottom vector holder used in call to the underlying SoftmaxLayer::Forward </div><div class="ttdef"><b>Definition:</b> infogain_loss_layer.hpp:126</div></div>
<div class="ttc" id="classcaffe_1_1InfogainLossLayer_html_ad3f7c2efdf32f99510186495ba7c5cff"><div class="ttname"><a href="classcaffe_1_1InfogainLossLayer.html#ad3f7c2efdf32f99510186495ba7c5cff">caffe::InfogainLossLayer::ignore_label_</a></div><div class="ttdeci">int ignore_label_</div><div class="ttdoc">The label indicating that an instance should be ignored. </div><div class="ttdef"><b>Definition:</b> infogain_loss_layer.hpp:136</div></div>
<div class="ttc" id="classcaffe_1_1InfogainLossLayer_html_a4ae881c2950ca84a50b0f964797defd6"><div class="ttname"><a href="classcaffe_1_1InfogainLossLayer.html#a4ae881c2950ca84a50b0f964797defd6">caffe::InfogainLossLayer::prob_</a></div><div class="ttdeci">Blob&lt; Dtype &gt; prob_</div><div class="ttdoc">prob stores the output probability predictions from the SoftmaxLayer. </div><div class="ttdef"><b>Definition:</b> infogain_loss_layer.hpp:124</div></div>
<div class="ttc" id="namespacecaffe_html"><div class="ttname"><a href="namespacecaffe.html">caffe</a></div><div class="ttdoc">A layer factory that allows one to register layers. During runtime, registered layers can be called b...</div><div class="ttdef"><b>Definition:</b> blob.hpp:14</div></div>
<div class="ttc" id="classcaffe_1_1InfogainLossLayer_html_aaf55e75f2296586b1fee0175e2d72fbb"><div class="ttname"><a href="classcaffe_1_1InfogainLossLayer.html#aaf55e75f2296586b1fee0175e2d72fbb">caffe::InfogainLossLayer::ExactNumTopBlobs</a></div><div class="ttdeci">virtual int ExactNumTopBlobs() const</div><div class="ttdoc">Returns the exact number of top blobs required by the layer, or -1 if no exact number is required...</div><div class="ttdef"><b>Definition:</b> infogain_loss_layer.hpp:66</div></div>
<div class="ttc" id="classcaffe_1_1InfogainLossLayer_html_aada26ffd60207582fe2af602004e271b"><div class="ttname"><a href="classcaffe_1_1InfogainLossLayer.html#aada26ffd60207582fe2af602004e271b">caffe::InfogainLossLayer::type</a></div><div class="ttdeci">virtual const char * type() const</div><div class="ttdoc">Returns the layer type. </div><div class="ttdef"><b>Definition:</b> infogain_loss_layer.hpp:70</div></div>
<div class="ttc" id="classcaffe_1_1InfogainLossLayer_html_ab7fe88c996d31d67f5e13fc0ffc803c2"><div class="ttname"><a href="classcaffe_1_1InfogainLossLayer.html#ab7fe88c996d31d67f5e13fc0ffc803c2">caffe::InfogainLossLayer::normalization_</a></div><div class="ttdeci">LossParameter_NormalizationMode normalization_</div><div class="ttdoc">How to normalize the output loss. </div><div class="ttdef"><b>Definition:</b> infogain_loss_layer.hpp:138</div></div>
<div class="ttc" id="classcaffe_1_1InfogainLossLayer_html"><div class="ttname"><a href="classcaffe_1_1InfogainLossLayer.html">caffe::InfogainLossLayer</a></div><div class="ttdoc">A generalization of MultinomialLogisticLossLayer that takes an &quot;information gain&quot; (infogain) matrix s...</div><div class="ttdef"><b>Definition:</b> infogain_loss_layer.hpp:48</div></div>
<div class="ttc" id="classcaffe_1_1InfogainLossLayer_html_a6d8fc17daa7233fb96629b641fbc46ac"><div class="ttname"><a href="classcaffe_1_1InfogainLossLayer.html#a6d8fc17daa7233fb96629b641fbc46ac">caffe::InfogainLossLayer::Backward_cpu</a></div><div class="ttdeci">virtual void Backward_cpu(const vector&lt; Blob&lt; Dtype &gt; *&gt; &amp;top, const vector&lt; bool &gt; &amp;propagate_down, const vector&lt; Blob&lt; Dtype &gt; *&gt; &amp;bottom)</div><div class="ttdoc">Computes the infogain loss error gradient w.r.t. the predictions. </div><div class="ttdef"><b>Definition:</b> infogain_loss_layer.cpp:168</div></div>
<div class="ttc" id="classcaffe_1_1InfogainLossLayer_html_a1992ffcac64ab6d61ebce80c7d9e8405"><div class="ttname"><a href="classcaffe_1_1InfogainLossLayer.html#a1992ffcac64ab6d61ebce80c7d9e8405">caffe::InfogainLossLayer::softmax_layer_</a></div><div class="ttdeci">shared_ptr&lt; Layer&lt; Dtype &gt; &gt; softmax_layer_</div><div class="ttdoc">The internal SoftmaxLayer used to map predictions to a distribution. </div><div class="ttdef"><b>Definition:</b> infogain_loss_layer.hpp:122</div></div>
<div class="ttc" id="classcaffe_1_1InfogainLossLayer_html_a9b2372959a16da1e80ae7a98b7689a4c"><div class="ttname"><a href="classcaffe_1_1InfogainLossLayer.html#a9b2372959a16da1e80ae7a98b7689a4c">caffe::InfogainLossLayer::MaxBottomBlobs</a></div><div class="ttdeci">virtual int MaxBottomBlobs() const</div><div class="ttdoc">Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is requi...</div><div class="ttdef"><b>Definition:</b> infogain_loss_layer.hpp:62</div></div>
<div class="ttc" id="classcaffe_1_1InfogainLossLayer_html_a421720fc0f85daf8b6b7808719b1f9e8"><div class="ttname"><a href="classcaffe_1_1InfogainLossLayer.html#a421720fc0f85daf8b6b7808719b1f9e8">caffe::InfogainLossLayer::has_ignore_label_</a></div><div class="ttdeci">bool has_ignore_label_</div><div class="ttdoc">Whether to ignore instances with a certain label. </div><div class="ttdef"><b>Definition:</b> infogain_loss_layer.hpp:134</div></div>
<div class="ttc" id="classcaffe_1_1InfogainLossLayer_html_abce0ff34e57ed3660e39633535c97e41"><div class="ttname"><a href="classcaffe_1_1InfogainLossLayer.html#abce0ff34e57ed3660e39633535c97e41">caffe::InfogainLossLayer::softmax_top_vec_</a></div><div class="ttdeci">vector&lt; Blob&lt; Dtype &gt; * &gt; softmax_top_vec_</div><div class="ttdoc">top vector holder used in call to the underlying SoftmaxLayer::Forward </div><div class="ttdef"><b>Definition:</b> infogain_loss_layer.hpp:128</div></div>
<div class="ttc" id="classcaffe_1_1InfogainLossLayer_html_a134b51c126eb4b62fac804965f8d8327"><div class="ttname"><a href="classcaffe_1_1InfogainLossLayer.html#a134b51c126eb4b62fac804965f8d8327">caffe::InfogainLossLayer::Forward_cpu</a></div><div class="ttdeci">virtual void Forward_cpu(const vector&lt; Blob&lt; Dtype &gt; *&gt; &amp;bottom, const vector&lt; Blob&lt; Dtype &gt; *&gt; &amp;top)</div><div class="ttdoc">A generalization of MultinomialLogisticLossLayer that takes an &quot;information gain&quot; (infogain) matrix s...</div><div class="ttdef"><b>Definition:</b> infogain_loss_layer.cpp:129</div></div>
<div class="ttc" id="classcaffe_1_1InfogainLossLayer_html_aa03732f381764180748479c83b289869"><div class="ttname"><a href="classcaffe_1_1InfogainLossLayer.html#aa03732f381764180748479c83b289869">caffe::InfogainLossLayer::ExactNumBottomBlobs</a></div><div class="ttdeci">virtual int ExactNumBottomBlobs() const</div><div class="ttdoc">Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...</div><div class="ttdef"><b>Definition:</b> infogain_loss_layer.hpp:60</div></div>
<div class="ttc" id="classcaffe_1_1InfogainLossLayer_html_a93019601c6256354fd4758da91d9311f"><div class="ttname"><a href="classcaffe_1_1InfogainLossLayer.html#a93019601c6256354fd4758da91d9311f">caffe::InfogainLossLayer::MaxTopBlobs</a></div><div class="ttdeci">virtual int MaxTopBlobs() const</div><div class="ttdoc">Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required...</div><div class="ttdef"><b>Definition:</b> infogain_loss_layer.hpp:68</div></div>
<div class="ttc" id="classcaffe_1_1InfogainLossLayer_html_a030296e6af30acd17a3cfe4463456147"><div class="ttname"><a href="classcaffe_1_1InfogainLossLayer.html#a030296e6af30acd17a3cfe4463456147">caffe::InfogainLossLayer::sum_rows_of_H</a></div><div class="ttdeci">virtual void sum_rows_of_H(const Blob&lt; Dtype &gt; *H)</div><div class="ttdoc">fill sum_rows_H_ according to matrix H </div><div class="ttdef"><b>Definition:</b> infogain_loss_layer.cpp:115</div></div>
<div class="ttc" id="classcaffe_1_1LossLayer_html"><div class="ttname"><a href="classcaffe_1_1LossLayer.html">caffe::LossLayer</a></div><div class="ttdoc">An interface for Layers that take two Blobs as input – usually (1) predictions and (2) ground-truth ...</div><div class="ttdef"><b>Definition:</b> loss_layer.hpp:23</div></div>
<div class="ttc" id="classcaffe_1_1InfogainLossLayer_html_a0e5e9667b19fb88ece7298e3e83d2fdb"><div class="ttname"><a href="classcaffe_1_1InfogainLossLayer.html#a0e5e9667b19fb88ece7298e3e83d2fdb">caffe::InfogainLossLayer::get_normalizer</a></div><div class="ttdeci">virtual Dtype get_normalizer(LossParameter_NormalizationMode normalization_mode, int valid_count)</div><div class="ttdef"><b>Definition:</b> infogain_loss_layer.cpp:85</div></div>
<div class="ttc" id="classcaffe_1_1InfogainLossLayer_html_ad8a1ef702a695e379e5d0450369b4a0c"><div class="ttname"><a href="classcaffe_1_1InfogainLossLayer.html#ad8a1ef702a695e379e5d0450369b4a0c">caffe::InfogainLossLayer::MinBottomBlobs</a></div><div class="ttdeci">virtual int MinBottomBlobs() const</div><div class="ttdoc">Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is requi...</div><div class="ttdef"><b>Definition:</b> infogain_loss_layer.hpp:61</div></div>
<div class="ttc" id="classcaffe_1_1InfogainLossLayer_html_a83ed478450bc7f629499fed37f654c5c"><div class="ttname"><a href="classcaffe_1_1InfogainLossLayer.html#a83ed478450bc7f629499fed37f654c5c">caffe::InfogainLossLayer::Reshape</a></div><div class="ttdeci">virtual void Reshape(const vector&lt; Blob&lt; Dtype &gt; *&gt; &amp;bottom, const vector&lt; Blob&lt; Dtype &gt; *&gt; &amp;top)</div><div class="ttdoc">Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...</div><div class="ttdef"><b>Definition:</b> infogain_loss_layer.cpp:51</div></div>
<div class="ttc" id="classcaffe_1_1Blob_html"><div class="ttname"><a href="classcaffe_1_1Blob.html">caffe::Blob</a></div><div class="ttdoc">A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...</div><div class="ttdef"><b>Definition:</b> blob.hpp:24</div></div>
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